


import argparse
import json
import os
from collections import OrderedDict

import torch

from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken



@torch.no_grad()
def UpperCamelCase_				(				__a			,				__a			,				__a			,				__a			,				__a      )	->   Tuple:
      # Load configuration defined in the metadata file
      with open(__a      ) as metadata_file:
            a__	:  Optional[int]     =   json.load(__a      )
      a__	:  str     =   LukeConfig(use_entity_aware_attention=__a			,				**metadata["model_config"]      )

      # Load in the weights from the checkpoint_path
      a__	:  List[Any]     =   torch.load(__a			,				map_location="cpu"      )["module"]

      # Load the entity vocab file
      a__	:  Union[str, Any]     =   load_original_entity_vocab(__a      )
      # add an entry for [MASK2]
      a__	:  List[Any]     =   max(entity_vocab.values()      ) + 1
      config.entity_vocab_size += 1

      a__	:  Optional[int]     =   XLMRobertaTokenizer.from_pretrained(metadata["model_config"]["bert_model_name"]      )

      # Add special tokens to the token vocabulary for downstream tasks
      a__	:  Union[str, Any]     =   AddedToken("<ent>"			,				lstrip=__a			,				rstrip=__a      )
      a__	:  Dict     =   AddedToken("<ent2>"			,				lstrip=__a			,				rstrip=__a      )
      tokenizer.add_special_tokens({"additional_special_tokens": [entity_token_a, entity_token_a]}      )
      config.vocab_size += 2

      print(f'''Saving tokenizer to {pytorch_dump_folder_path}'''      )
      tokenizer.save_pretrained(__a      )
      with open(os.path.join(__a			,				"tokenizer_config.json"      )			,				"r"      ) as f:
            a__	:  int     =   json.load(__a      )
      a__	:  List[str]     =   "MLukeTokenizer"
      with open(os.path.join(__a			,				"tokenizer_config.json"      )			,				"w"      ) as f:
            json.dump(__a			,				__a      )

      with open(os.path.join(__a			,				MLukeTokenizer.vocab_files_names["entity_vocab_file"]      )			,				"w"      ) as f:
            json.dump(__a			,				__a      )

      a__	:  Dict     =   MLukeTokenizer.from_pretrained(__a      )

      # Initialize the embeddings of the special tokens
      a__	:  Dict     =   tokenizer.convert_tokens_to_ids(["@"]      )[0]
      a__	:  Union[str, Any]     =   tokenizer.convert_tokens_to_ids(["#"]      )[0]

      a__	:  List[str]     =   state_dict["embeddings.word_embeddings.weight"]
      a__	:  Optional[int]     =   word_emb[ent_init_index].unsqueeze(0      )
      a__	:  Union[str, Any]     =   word_emb[enta_init_index].unsqueeze(0      )
      a__	:  List[Any]     =   torch.cat([word_emb, ent_emb, enta_emb]      )
      # add special tokens for 'entity_predictions.bias'
      for bias_name in ["lm_head.decoder.bias", "lm_head.bias"]:
            a__	:  str     =   state_dict[bias_name]
            a__	:  Optional[int]     =   decoder_bias[ent_init_index].unsqueeze(0      )
            a__	:  str     =   decoder_bias[enta_init_index].unsqueeze(0      )
            a__	:  Tuple     =   torch.cat([decoder_bias, ent_decoder_bias, enta_decoder_bias]      )

      # Initialize the query layers of the entity-aware self-attention mechanism
      for layer_index in range(config.num_hidden_layers      ):
            for matrix_name in ["query.weight", "query.bias"]:
                  a__	:  Optional[Any]     =   f'''encoder.layer.{layer_index}.attention.self.'''
                  a__	:  List[str]     =   state_dict[prefix + matrix_name]
                  a__	:  str     =   state_dict[prefix + matrix_name]
                  a__	:  Any     =   state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
      a__	:  List[Any]     =   state_dict["entity_embeddings.entity_embeddings.weight"]
      a__	:  Union[str, Any]     =   entity_emb[entity_vocab["[MASK]"]].unsqueeze(0      )
      a__	:  List[Any]     =   torch.cat([entity_emb, entity_mask_emb]      )
      # add [MASK2] for 'entity_predictions.bias'
      a__	:  List[str]     =   state_dict["entity_predictions.bias"]
      a__	:  Dict     =   entity_prediction_bias[entity_vocab["[MASK]"]].unsqueeze(0      )
      a__	:  Any     =   torch.cat([entity_prediction_bias, entity_mask_bias]      )

      a__	:  int     =   LukeForMaskedLM(config=__a      ).eval()

      state_dict.pop("entity_predictions.decoder.weight"      )
      state_dict.pop("lm_head.decoder.weight"      )
      state_dict.pop("lm_head.decoder.bias"      )
      a__	:  Dict     =   OrderedDict()
      for key, value in state_dict.items():
            if not (key.startswith("lm_head"      ) or key.startswith("entity_predictions"      )):
                  a__	:  Union[str, Any]     =   state_dict[key]
            else:
                  a__	:  Dict     =   state_dict[key]

      a__,	a__	:  List[str]     =   model.load_state_dict(__a			,				strict=__a      )

      if set(__a      ) != {"luke.embeddings.position_ids"}:
            raise ValueError(f'''Unexpected unexpected_keys: {unexpected_keys}'''      )
      if set(__a      ) != {
          "lm_head.decoder.weight",
          "lm_head.decoder.bias",
          "entity_predictions.decoder.weight",
      }:
            raise ValueError(f'''Unexpected missing_keys: {missing_keys}'''      )

      model.tie_weights()
      assert (model.luke.embeddings.word_embeddings.weight == model.lm_head.decoder.weight).all()
      assert (model.luke.entity_embeddings.entity_embeddings.weight == model.entity_predictions.decoder.weight).all()

      # Check outputs
      a__	:  int     =   MLukeTokenizer.from_pretrained(__a			,				task="entity_classification"      )

      a__	:  int     =   "ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan)."
      a__	:  Optional[Any]     =   (0, 9)
      a__	:  int     =   tokenizer(__a			,				entity_spans=[span]			,				return_tensors="pt"      )

      a__	:  Optional[int]     =   model(**__a      )

      # Verify word hidden states
      if model_size == "large":
            raise NotImplementedError
      else:  # base
            a__	:  List[Any]     =   torch.Size((1, 33, 768)      )
            a__	:  int     =   torch.tensor([[0.0892, 0.0596, -0.2819], [0.0134, 0.1199, 0.0573], [-0.0169, 0.0927, 0.0644]]      )

      if not (outputs.last_hidden_state.shape == expected_shape):
            raise ValueError(
                f'''Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}'''      )
      if not torch.allclose(outputs.last_hidden_state[0, :3, :3]			,				__a			,				atol=1e-4      ):
            raise ValueError

      # Verify entity hidden states
      if model_size == "large":
            raise NotImplementedError
      else:  # base
            a__	:  str     =   torch.Size((1, 1, 768)      )
            a__	:  Any     =   torch.tensor([[-0.1482, 0.0609, 0.0322]]      )

      if not (outputs.entity_last_hidden_state.shape == expected_shape):
            raise ValueError(
                f'''Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is'''
                f''' {expected_shape}'''      )
      if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3]			,				__a			,				atol=1e-4      ):
            raise ValueError

      # Verify masked word/entity prediction
      a__	:  int     =   MLukeTokenizer.from_pretrained(__a      )
      a__	:  int     =   "Tokyo is the capital of <mask>."
      a__	:  Any     =   (24, 30)
      a__	:  Optional[int]     =   tokenizer(__a			,				entity_spans=[span]			,				return_tensors="pt"      )

      a__	:  Union[str, Any]     =   model(**__a      )

      a__	:  Any     =   encoding["input_ids"][0].tolist()
      a__	:  int     =   input_ids.index(tokenizer.convert_tokens_to_ids("<mask>"      )      )
      a__	:  Any     =   outputs.logits[0][mask_position_id].argmax(dim=-1      )
      assert "Japan" == tokenizer.decode(__a      )

      a__	:  List[Any]     =   outputs.entity_logits[0][0].argmax().item()
      a__	:  Tuple     =   [
          entity for entity, entity_id in tokenizer.entity_vocab.items() if entity_id == predicted_entity_id
      ]
      assert [e for e in multilingual_predicted_entities if e.startswith("en:"      )][0] == "en:Japan"

      # Finally, save our PyTorch model and tokenizer
      print("Saving PyTorch model to {}".format(__a      )      )
      model.save_pretrained(__a      )



def UpperCamelCase_				(				__a      )	->   int:
      a__	:  str     =   ["[MASK]", "[PAD]", "[UNK]"]

      a__	:  List[Any]     =   [json.loads(__a      ) for line in open(__a      )]

      a__	:  Optional[Any]     =   {}
      for entry in data:
            a__	:  Optional[int]     =   entry["id"]
            for entity_name, language in entry["entities"]:
                  if entity_name in SPECIAL_TOKENS:
                        a__	:  Dict     =   entity_id
                        break
                  a__	:  Union[str, Any]     =   f'''{language}:{entity_name}'''
                  a__	:  Union[str, Any]     =   entity_id
      return new_mapping


if __name__ == "__main__":
    UpperCamelCase					:       Dict			    =     argparse.ArgumentParser()
    # Required parameters
    parser.add_argument("""--checkpoint_path""", type=str, help="""Path to a pytorch_model.bin file.""")
    parser.add_argument(
        """--metadata_path""", default=None, type=str, help="""Path to a metadata.json file, defining the configuration."""
    )
    parser.add_argument(
        """--entity_vocab_path""",
        default=None,
        type=str,
        help="""Path to an entity_vocab.tsv file, containing the entity vocabulary.""",
    )
    parser.add_argument(
        """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to where to dump the output PyTorch model."""
    )
    parser.add_argument(
        """--model_size""", default="""base""", type=str, choices=["""base""", """large"""], help="""Size of the model to be converted."""
    )
    UpperCamelCase					:       Tuple			    =     parser.parse_args()
    convert_luke_checkpoint(
        args.checkpoint_path,
        args.metadata_path,
        args.entity_vocab_path,
        args.pytorch_dump_folder_path,
        args.model_size,
    )









"""simple docstring"""
def 							A						(				__snake_case: int  )    ->		int:

       """simple docstring"""





       if divisor % 5 == 0 or divisor % 2 == 0:
              return 0
       __magic_name__      				=    1
       __magic_name__      				=    1
       while repunit:
              __magic_name__      				=    (1_0 * repunit + 1) % divisor
              repunit_index += 1
       return repunit_index


def 							A						(				__snake_case: int = 1_0_0_0_0_0_0  )    ->		int:

       """simple docstring"""





       __magic_name__      				=    limit - 1
       if divisor % 2 == 0:
              divisor += 1
       while least_divisible_repunit(__snake_case  ) <= limit:
              divisor += 2
       return divisor


if __name__ == "__main__":
 print(f"""{solution() = }""")